Thermostatically controlled load (TCL, such as heating, ventilation, and air conditioning system) is a desirable demand-side flexibility source in distribution networks. It can participate in regulation services and mitigate power imbalances from fluctuating distributed renewable generation. To effectively utilize the load flexibility from spatially and temporally distributed TCLs in a distribution network, it is necessary to consider power flow constraints to avoid possible voltage or current violations. Published works usually adopt optimal power flow models (OPF) to describe these constraints. However, these models require accurate topology of the distribution network that is often unobservable in practice. To bypass this challenge, this paper proposes a novel learning-based OPF to optimize TCLs for regulation services. This method trains three regression multi-layer perceptrons (MLPs) based on the distribution network's historical operation data to replicate its power flow constraints. The trained MLPs are further equivalently reformulated into linear constraints with binary variables so that the optimization problem becomes a mixed-integer linear program that can be effectively solved. Numerical experiments based on the IEEE 123-bus system validate that the proposed method can achieve better TCL power scheduling performance with guaranteed feasibility and optimality than other state-of-art models.

中文翻译：

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基于基于学习的最优潮流模型调度恒温控制负载以提供调节能力


恒温控制负载（TCL，例如供暖、通风和空调系统）是配电网络中理想的需求侧灵活性来源。它可以参与监管服务并缓解分布式可再生能源发电波动造成的电力失衡。为了有效利用配电网络中空间和时间分布的 TCL 的负载灵活性，有必要考虑潮流约束以避免可能的电压或电流违规。已发表的作品通常采用最优潮流模型（OPF）来描述这些约束。然而，这些模型需要准确的配电网络拓扑，而这在实践中通常是不可观测的。为了绕过这一挑战，本文提出了一种新颖的基于学习的 OPF 来优化 TCL 的监管服务。该方法根据配电网的历史运行数据训练三个回归多层感知器（MLP）来复制其潮流约束。训练好的 MLP 进一步等价地重新表述为具有二元变量的线性约束，使得优化问题成为可以有效求解的混合整数线性规划。基于IEEE 123总线系统的数值实验验证了所提出的方法可以实现更好的TCL功率调度性能，并且比其他最先进的模型具有保证的可行性和最优性。